datatonic - Principal Data Architect

London, Greater London, United Kingdom2w ago

In Office Principal EMEA Cloud Computing Data Analytics Data Architect Principal Java SQL Data Quality Python Data Governance

Requirements

• Data Architecture – Proven experience designing and building data warehouse / lakehouse solutions using technologies like BigQuery, Azure Synapse, Snowflake, Databricks • Data Modeling – Strong expertise in data modeling and solution architecture, optimizing for performance and scalability • Data Governance - Experience with data platforms with data quality, security, privacy, and governance controls built-in • Ownership Mindset – Ability to take projects from concept to completion, driving creative and effective solutions • Analytical & Technical Excellence – Demonstrated problem-solving skills with a strong technical foundation and an innovative approach • Communication & Presentation – Exceptional written and verbal communication skills with great attention to detail, capable of presenting complex concepts clearly to customers • Stakeholder Management – Ability to build and maintain strong relationships with key external stakeholders across different business levels • Programming Proficiency – Hands-on experience with Python, Java, and SQL for data engineering and solution development

Responsibilities

• Design & Deliver Cutting-Edge Data Solutions – Lead the analysis, design, and execution of state-of-the-art, data-driven solutions to meet our client’s business needs, leveraging the best of Google Cloud technologies. • Data Architecture & Governance – Serve as an expert in data transformation, storage, retrieval, security, and governance, ensuring scalable, secure, and efficient data solutions. • Guide & Mentor Engineers – Provide architectural direction to engineers, ensuring they build robust, high-performance solutions aligned with your target data architecture. • Master Data Modeling Techniques – Apply expertise in various data modeling approaches, including 3NF, Data Vault, Star Schema, and One Big Table (OBT). Clearly articulate the benefits and trade-offs of each method and optimize their implementation within columnar databases such as BigQuery. • Shape Data Strategy – Collaborate with the client to define and refine data strategy, covering: • Data governance and compliance • Scalable and efficient data modeling techniques • Ensuring data quality and integrity • Data management, security, and privacy best practices • Establishing optimal workflows and operational efficiencies • Develop Fully Integrated Solutions – Work alongside Architecture, Engineering, and Data Science teams to design comprehensive, production-ready solutions that incorporate: • Cloud best practices • Scalable and efficient ingestion strategies • Feature engineering methodologies • End-to-end production readiness • Leverage Leading Technologies – Design and implement solutions using key partner technologies, including: • Google Cloud – BigQuery, Dataflow, Vertex AI, and more • dbt Labs – Modern analytics engineering and transformation • Snowflake – Cloud-native data warehousing • Fivetran – Automated data pipelines for seamless integration